As Bike Share Schemes around the world become more popular, how we manage the resources such as bikes and docking stations defines the success and growth of such programs.

For Bike Share Schemes to truly be a solution to last mile problems, riders need bikes and docking stations to be available when and where they need them. It is up to the operators to ensure this happens every time.

But many operators fail to provide this basic level of service as they lack the actionable data and operations to manage the schemes effectively.

For a long time, the solution to ridership problems in Bike Share Schemes has been to supply the market with more bikes. In reality this does little to increase efficiency and often adds to the problem.

Mobike’s ‘Magic Cube’, uses data and AI to forecast supply and demand for its bike-rentals. In a fierce competition for market share, Mobike is seeing the value of using AI to simplify scheduling and operations of its scheme.

The importance of data and AI is clear. For operators, the key is in not only collecting the data but also having a process that works with its systems and resources to drive growth and increase ridership.

In the future, we are going to see more operators turn to data and AI, especially since cities have the potential to collect and store vast amounts of valuable data. With actionable data, operators save money, cities aren’t cluttered with bikes and citizens can rely on a reliable Bike Share Scheme that they can use in their day-to-day lives.

At Stage Intelligence, we have been using Artificial Intelligence (AI) and self-organising algorithms to solve complex problems in Bike Share Schemes from the beginning. Our BICO solution is easily incorporated into existing platforms to simplify logistics and increase ridership.